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Google Professional-Machine-Learning-Engineer: Google Professional Machine Learning Engineer

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Question 1

You are designing an architecture with a serveress ML system to enrich customer support tickets with informative metadata before they are routed to a support agent. You need a set of models to predict ticket priority, predict ticket resolution time, and perform sentiment analysis to help agents make strategic decisions when they process support requests. Tickets are not expected to have any domain-specific terms or jargon.
The proposed architecture has the following flow:
Professional-Machine-Learning-Engineer dumps exhibit
Which endpoints should the Enrichment Cloud Functions call?

A. 1 = Al Platform, 2 = Al Platform, 3 = AutoML Vision

B. 1 = Al Platform, 2 = Al Platform, 3 = AutoML Natural Language

C. 1 = Al Platform, 2 = Al Platform, 3 = Cloud Natural Language API

D. 1 = cloud Natural Language API, 2 = Al Platform, 3 = Cloud Vision API

Correct Answer:B

Question 2

Your team has been tasked with creating an ML solution in Google Cloud to classify support requests for one of your platforms. You analyzed the requirements and decided to use TensorFlow to build the classifier so that you have full control of the model's code, serving, and deployment. You will use Kubeflow pipelines for the ML platform. To save time, you want to build on existing resources and use managed services instead of building a completely new model. How should you build the classifier?

A. Use the Natural Language API to classify support requests

B. Use AutoML Natural Language to build the support requests classifier

C. Use an established text classification model on Al Platform to perform transfer learning

D. Use an established text classification model on Al Platform as-is to classify support requests

Correct Answer:D

Question 3

You are training an LSTM-based model on Al Platform to summarize text using the following job submission script:
Professional-Machine-Learning-Engineer dumps exhibit
You want to ensure that training time is minimized without significantly compromising the accuracy of your model. What should you do?

A. Modify the 'epochs' parameter

B. Modify the 'scale-tier' parameter

C. Modify the batch size' parameter

D. Modify the 'learning rate' parameter

Correct Answer:A

Question 4

You work for a global footwear retailer and need to predict when an item will be out of stock based on historical inventory data. Customer behavior is highly dynamic since footwear demand is influenced by many different factors. You want to serve models that are trained on all available data, but track your performance on specific subsets of data before pushing to production. What is the most streamlined and reliable way to perform this validation?

A. Use the TFX ModelValidator tools to specify performance metrics for production readiness

B. Use k-fold cross-validation as a validation strategy to ensure that your model is ready for production.

C. Use the last relevant week of data as a validation set to ensure that your model is performing accurately on current data

D. Use the entire dataset and treat the area under the receiver operating characteristics curve (AUC ROC) as the main metric.

Correct Answer:A

Question 5

You are an ML engineer at a global car manufacturer. You need to build an ML model to predict car sales in different cities around the world. Which features or feature crosses should you use to train city-specific relationships between car type and number of sales?

A. Three individual features binned latitude, binned longitude, and one-hot encoded car type

B. One feature obtained as an element-wise product between latitude, longitude, and car type

C. One feature obtained as an element-wise product between binned latitude, binned longitude, and one-hot encoded car type

D. Two feature crosses as a element-wise product the first between binned latitude and one-hot encoded car type, and the second between binned longitude and one-hot encoded car type

Correct Answer:A

Question 6

You are going to train a DNN regression model with Keras APIs using this code:
Professional-Machine-Learning-Engineer dumps exhibit
How many trainable weights does your model have? (The arithmetic below is correct.)

A. 501*256+257*128+2 = 161154

B. 500*256+256*128+128*2 = 161024

C. 501*256+257*128+128*2=161408

D. 500*256*0 25+256*128*0 25+128*2 = 40448

Correct Answer:D

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